While traditionally modeled by counting of context words, predictive
models have eventually taken the clear lead in terms of performance
[1]}. Neural network language models in particular
have proved useful for semantic modeling, and are especially practical
to incorporate into deep learning setups due to their dense vectors
and the unified neural framework for learning. Mikolov et al. [2]}
have put forward an efficient neural method that can learn highly
accurate word vectors as it can train on massive data sets in practical
time (a billion words in the order of a day on standard architecture).
Subsequently, Le & Mikolov [3]} extended the
model in order to represent compositional semantics (cf. [4]})
of sequences of words, from sentences to the length of documents,
which they demonstrated to provide state-of-the-art performance on
sentiment analysis of movie reviews. Analogous to that task, we employ
their distributed memory method to learn document vectors for news
articles and use them for learning to predict coinciding distress,
as a type of risk sentiment analysis guided by the event data we provide.
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